{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ebd66700",
   "metadata": {},
   "source": [
    "## Demo_Frequency\n",
    "This is a demo for visualizing the Frequency saliency map of a Neuron Network\n",
    "\n",
    "To run this demo from scratch, you need first generate a BadNet attack result by using the following cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b950f4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "! python ../../attack/badnet.py - -save_folder_name badnet_demo\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f81f973",
   "metadata": {},
   "source": [
    "or run the following command in your terminal\n",
    "\n",
    "```python attack/badnet.py --save_folder_name badnet_demo```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bd9f5a",
   "metadata": {},
   "source": [
    "### Step 1: Import modules and set arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "71b7087b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys, os\n",
    "import yaml\n",
    "import torch\n",
    "import numpy as np\n",
    "import torchvision.transforms as transforms\n",
    "import matplotlib as mlp\n",
    "sys.path.append(\"../\")\n",
    "sys.path.append(\"../../\")\n",
    "sys.path.append(os.getcwd())\n",
    "from visual_utils import *\n",
    "from utils.aggregate_block.dataset_and_transform_generate import (\n",
    "    get_transform,\n",
    "    get_dataset_denormalization,\n",
    ")\n",
    "from utils.aggregate_block.fix_random import fix_random\n",
    "from utils.aggregate_block.model_trainer_generate import generate_cls_model\n",
    "from utils.save_load_attack import load_attack_result\n",
    "from utils.defense_utils.dbd.model.utils import (\n",
    "    get_network_dbd,\n",
    "    load_state,\n",
    "    get_criterion,\n",
    "    get_optimizer,\n",
    "    get_scheduler,\n",
    ")\n",
    "from utils.defense_utils.dbd.model.model import SelfModel, LinearModel\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2fb719c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Basic setting: args\n",
    "args = get_args(True)\n",
    "\n",
    "########## For Demo Only ##########\n",
    "args.yaml_path = \"../../\"+args.yaml_path\n",
    "args.result_file_attack = \"badnet_demo\"\n",
    "######## End For Demo Only ##########\n",
    "\n",
    "with open(args.yaml_path, \"r\") as stream:\n",
    "    config = yaml.safe_load(stream)\n",
    "config.update({k: v for k, v in args.__dict__.items() if v is not None})\n",
    "args.__dict__ = config\n",
    "args = preprocess_args(args)\n",
    "fix_random(int(args.random_seed))\n",
    "\n",
    "save_path_attack = \"../..//record/\" + args.result_file_attack\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f959b510",
   "metadata": {},
   "source": [
    "### Step 2: Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8b67ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:save_path MUST have 'record' in its abspath, and data_path in attack result MUST have 'data' in its path\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n",
      "loading...\n",
      "max_num_samples is given, use sample number limit now.\n",
      "subset bd dataset with length:  4995\n",
      "Create visualization dataset with \n",
      " \t Dataset: bd_test \n",
      " \t Number of samples: 4995  \n",
      " \t Selected classes: [0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "# Load result\n",
    "result_attack = load_attack_result(save_path_attack + \"/attack_result.pt\")\n",
    "selected_classes = np.arange(args.num_classes)\n",
    "\n",
    "# Select classes to visualize\n",
    "if args.num_classes > args.c_sub:\n",
    "    selected_classes = np.delete(selected_classes, args.target_class)\n",
    "    selected_classes = np.random.choice(\n",
    "        selected_classes, args.c_sub-1, replace=False)\n",
    "    selected_classes = np.append(selected_classes, args.target_class)\n",
    "\n",
    "# keep the same transforms for train and test dataset for better visualization\n",
    "result_attack[\"clean_train\"].wrap_img_transform = result_attack[\"clean_test\"].wrap_img_transform \n",
    "result_attack[\"bd_train\"].wrap_img_transform = result_attack[\"bd_test\"].wrap_img_transform \n",
    "\n",
    "# Create dataset\n",
    "args.visual_dataset = 'bd_test'\n",
    "if args.visual_dataset == 'mixed':\n",
    "    bd_test_with_trans = result_attack[\"bd_test\"]\n",
    "    visual_dataset = generate_mix_dataset(\n",
    "        bd_test_with_trans, args.target_class, args.pratio, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'clean_train':\n",
    "    clean_train_with_trans = result_attack[\"clean_train\"]\n",
    "    visual_dataset = generate_clean_dataset(\n",
    "        clean_train_with_trans, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'clean_test':\n",
    "    clean_test_with_trans = result_attack[\"clean_test\"]\n",
    "    visual_dataset = generate_clean_dataset(\n",
    "        clean_test_with_trans, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'bd_train':\n",
    "    bd_train_with_trans = result_attack[\"bd_train\"]\n",
    "    visual_dataset = generate_bd_dataset(\n",
    "        bd_train_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n",
    "elif args.visual_dataset == 'bd_test':\n",
    "    bd_test_with_trans = result_attack[\"bd_test\"]\n",
    "    visual_dataset = generate_bd_dataset(\n",
    "        bd_test_with_trans, args.target_class, selected_classes, max_num_samples=args.n_sub)\n",
    "else:\n",
    "    assert False, \"Illegal vis_class\"\n",
    "\n",
    "print(\n",
    "    f'Create visualization dataset with \\n \\t Dataset: {args.visual_dataset} \\n \\t Number of samples: {len(visual_dataset)}  \\n \\t Selected classes: {selected_classes}')\n",
    "\n",
    "# Create data loader\n",
    "data_loader = torch.utils.data.DataLoader(\n",
    "    visual_dataset, batch_size=args.batch_size, num_workers=args.num_workers, shuffle=False\n",
    ")\n",
    "\n",
    "# Create denormalization function\n",
    "for trans_t in data_loader.dataset.wrap_img_transform.transforms:\n",
    "    if isinstance(trans_t, transforms.Normalize):\n",
    "        denormalizer = get_dataset_denormalization(trans_t)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "39104beb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number Poisoned samples: 4995\n",
      "Select 2 poisoned samples\n",
      "Select 2 clean samples\n"
     ]
    }
   ],
   "source": [
    "# Choose samples to show SHAP values. By Default, 2 clean images + 2 Poison images. If no enough Poison images, use 4 clean images instead.AblationCAM\n",
    "total_num = 4\n",
    "bd_num = 0\n",
    "\n",
    "visual_samples = []\n",
    "visual_labels = []\n",
    "\n",
    "visual_poison_indicator = np.array(\n",
    "    get_poison_indicator_from_bd_dataset(visual_dataset))\n",
    "if visual_poison_indicator.sum() > 0:\n",
    "    print(f'Number Poisoned samples: {visual_poison_indicator.sum()}')\n",
    "    # random choose two poisoned samples\n",
    "    selected_bd_idx = np.random.choice(\n",
    "        np.where(visual_poison_indicator == 1)[0], 2, replace=False)\n",
    "    for i in selected_bd_idx:\n",
    "        visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n",
    "        visual_labels.append(visual_dataset[i][4])\n",
    "    bd_num = len(selected_bd_idx)\n",
    "    print(f'Select {bd_num} poisoned samples')\n",
    "\n",
    "# Trun all samples to clean\n",
    "with temporary_all_clean(visual_dataset):\n",
    "    # you can just set selected_clean_idx = selected_bd_idx to build the correspondence between clean samples and poisoned samples\n",
    "    selected_clean_idx = np.random.choice(\n",
    "        len(visual_dataset), total_num-bd_num, replace=False)\n",
    "    for i in selected_clean_idx:\n",
    "        visual_samples.append(visual_dataset[i][0].unsqueeze(0))\n",
    "        visual_labels.append(visual_dataset[i][1])\n",
    "    print(f'Select {len(selected_clean_idx)} clean samples')\n",
    "\n",
    "# Clean sample first\n",
    "visual_samples = visual_samples[::-1]\n",
    "visual_labels = visual_labels[::-1]\n",
    "\n",
    "visual_samples = torch.cat(visual_samples, axis=0).to(args.device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f652e5",
   "metadata": {},
   "source": [
    "### Step 3: Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ff67e7b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load model preactresnet18 from badnet_demo\n"
     ]
    }
   ],
   "source": [
    "# Load model\n",
    "model_visual = generate_cls_model(args.model, args.num_classes)\n",
    "model_visual.load_state_dict(result_attack[\"model\"])\n",
    "model_visual.to(args.device)\n",
    "# !!! Important to set eval mode !!!\n",
    "model_visual.eval()\n",
    "print(f\"Load model {args.model} from {args.result_file_attack}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc952077",
   "metadata": {},
   "source": [
    "### Step 4: Plot Frequency saliency map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94612903",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Frequency saliency map\n",
      "Choose layer layer4.1.conv2 from model preactresnet18\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "############ Frequency saliency map ################\n",
    "print('Plotting Frequency saliency map')\n",
    "\n",
    "# Choose layer for feature extraction\n",
    "module_dict = dict(model_visual.named_modules())\n",
    "target_layer = module_dict[args.target_layer_name]\n",
    "print(f'Choose layer {args.target_layer_name} from model {args.model}')\n",
    "\n",
    "sfm = torch.nn.Softmax(dim=1)\n",
    "outputs = model_visual(visual_samples)\n",
    "pre_p, pre_label = torch.max(sfm(outputs), dim=1)\n",
    "\n",
    "# get the names for the classes\n",
    "class_names = np.array(args.class_names).reshape([-1])\n",
    "\n",
    "frequency_maps = []\n",
    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 10))\n",
    "vnorm = mlp.colors.Normalize(vmin=0, vmax=255)\n",
    "for im in range(4):\n",
    "    rgb_image = np.swapaxes(\n",
    "        np.swapaxes(denormalizer(visual_samples[im]).cpu().numpy(), 0, 1), 1, 2\n",
    "    )\n",
    "    frequency_map = saliency(visual_samples[im], model_visual)\n",
    "    rgb_image[rgb_image < 1e-12] = 1e-12\n",
    "    axes[im // 2, im % 2 * 2].imshow(rgb_image)\n",
    "    axes[im // 2, im % 2 * 2].axis(\"off\")\n",
    "    if im == 0 or im == 1:\n",
    "        axes[im // 2, im % 2 * 2].set_title(\n",
    "            \"Clean Image: %s\" % (class_names[visual_labels[im]].capitalize())\n",
    "        )\n",
    "    else:\n",
    "        axes[im // 2, im % 2 * 2].set_title(\n",
    "            \"Poison Image: %s\" % (class_names[visual_labels[im]].capitalize())\n",
    "        )\n",
    "    image = axes[im // 2, im % 2 * 2 +\n",
    "                 1].imshow(frequency_map, cmap=plt.cm.coolwarm, norm=vnorm)\n",
    "    plt.colorbar(image, ax=axes[im // 2, im %\n",
    "                 2 * 2 + 1], orientation='vertical')\n",
    "    axes[im // 2, im % 2 * 2 + 1].axis(\"off\")\n",
    "    axes[im // 2, im % 2 * 2 + 1].set_title(\n",
    "        \"Predicted: %s, %.2f%%\" % (\n",
    "            class_names[pre_label[im]].capitalize(), pre_p[im] * 100)\n",
    "    )\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('py38')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "6869619afde5ccaa692f7f4d174735a0f86b1f7ceee086952855511b0b6edec0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
